10 Examples of Predictive Analytics

Proactivity is one of the most important factors in the success of many companies today. How can you be proactive while doing business? You need to anticipate what will happen in your business environment in the future and take corresponding actions. And the tool you need to do so is called predictive analytics. Let’s talk about it in more detail.

What Is Predictive Analytics?

The concept of predictive analytics is not new. It started with the first attempts of Henry Ford to analyze data in order to gain business insights in the late 19th century. In the 1960s, the interest in data analytics began to grow due to the penetration of computers into different industries.

In the late 1990s, the umbrella term business intelligence became widespread. The concept used various data science methodologies, including classification, statistics, analytics, modeling, visualization, data mining, etc. It offered software systems, such as ERP, to efficiently organize and process vast amounts of data. Business intelligence aims to provide a structured, comprehensive view of all the data within a company. Based on this data, managers and executives draw conclusions concerning past activities and can drive changes to improve processes in the future.

Predictive analytics is an extended version of business intelligence with the goal of predicting possible future events and taking proactive measures. On par with traditional data analysis, predictive analytics utilizes artificial intelligence, machine learning and deep learning models and can look at all potential scenarios without human interference. In addition, cloud technologies enable real-time data processing and speed up decision-making.

How Does Predictive Analytics Work?

The operation of predictive analytics is based on mathematical models, historical data and current data.

You need to take the following steps to apply the predictive analytics strategy to your business:

• Identify what problems need to be solved. You should clearly understand what you want to find out with the help of data. Ask yourself all the questions you want to get answers to. For example, “What groups of our customers are most likely to buy our new product?”
• Check if you have the necessary data. To make a predictive model work and produce adequate results, you should feed it with enough data. For example, to answer the question above, you need comprehensive information on your customers (personal information, buying history, interactions with your brand, etc.). Note that collected data should be structured and clean, and should cover a certain period (e.g., one year) so that an analytics model could learn and identify patterns correctly.
• Build and teach predictive analytics models. You should create a system based on DL, ML, or AI algorithms and your requirements. The system should be able to learn from historical data and analyze operational data to identify patterns and make predictions. Note that you should retrain the model from time to time, feeding the updated information to it because business environments change under various factors.
• Put insights into action. Predictive analytics will only bring benefits if you act according to the insights the system provides. That’s why you need responsible managers who will make decisions based on given predictions.

In simple words, the operational scheme of predictive analytics is as follows: purposefully created mathematical models or neural networks based on various algorithms discover patterns and trends from historical data and then process new data searching for the same patterns or trends. In the output, these models or networks provide predictions about what is most likely to happen regarding the given question/problem.

Examples of Predictive Analytics

Virtually any industry can apply predictive analytics to impact various business aspects positively. Precisely, companies can:

• better understand customers
• reduce risks
• eliminate inefficiencies
• streamline operations
• enhance productivity
• increase revenues.

Let’s look at the most common examples of predictive analytics across industries.

1. Retail

At present, retailers are probably the leading users of predictive analytics applications. Dynamic retail businesses must continuously monitor their customer behavior and market trends to adjust to changes and provide relevant responses quickly.

The role of predictive analytics in retail can’t be underrated. Predictive analytics software solutions help marketers and retail specialists at all stages of the buying journey. Take a look at the most common use cases.

• Predictive marketing. Advanced algorithms analyze market trends, buying habits and personal details of customers to further identify buying patterns and perform customer segmentation. Specialists can optimize marketing campaigns, create personalized recommendations and forecast sales based on such insights. Such analytics results in increased income and improved customer retention.
• Predictive inventory. Intelligent analytics algorithms analyze various factors (region, season, buying habits) to forecast the demand for various products. In this way, retailers determine the optimal inventory level to meet the demand, which helps them avoid overstocking or, on the contrary, running out of needed goods.
• Predictive supply chain. Predictive analytics algorithms help companies optimize several aspects of supply chains. Firstly, they make logistics more efficient by determining the fastest and most cost-efficient routes considering toll roads, traffic, weather conditions, etc. Secondly, trackers monitor fuel consumption and driving behavior, thus reducing transport costs. And thirdly, sensors monitor the conditions of machines and their components, anticipating technical maintenance and avoiding downtime.

2. Healthcare

An increasing number of medical institutions worldwide now implement software systems to their processes, meaning that they collect a wealth of data about patients and their health conditions. This provides a full range of opportunities for predictive analytics. By analyzing and comparing historical data to current data, intelligent algorithms can:

• reveal prerequisites for diseases and suggest preventive treatment
• predict the results of various treatments and choose the best option for each patient individually
• predict disease outbreaks and epidemics.

Such insights are critical for improving diagnoses and treatment, providing personalized patient care and consequently saving lives.

3. Internet of Things

Predictive analytics is tightly coupled with the Internet of Things since this technology collects tons of data that can be analyzed. The primary use case nowadays is predictive maintenance in smart manufacturing. IoT sensors installed on machines continuously collect data on their performance and send it to the processing platform where predictive models perform the analysis, identify abnormalities and suggest maintenance of specific spare parts. By applying such analytics, plants and factories eliminate equipment breakdowns and avoid downtime.

4. Sports

Predictive analytics is also gaining popularity in the sports industry. Professional teams (be it football, baseball, or basketball) hire data analysts in order to assess the performance of players and help team managers sign the most beneficial contracts.

Analytics specialists consider both on-field and off-field data and can predict each player’s value and regression. On-field metrics include a player’s physical performance such as speed, time, scoring, tactics, health conditions, etc. Off-field metrics refer to the business side of sports and provide insights on how much profit a player can bring to the team/club. This includes fan engagement, ticket sales, merchandise sales and so on. Off-field statistics are collected from various sources, including social media, ticket offices and distributors.

5. Weather

In the past decade, weather forecasts have become highly accurate due to predictive analytics. Intelligent models are fed with data collected throughout the history of meteorological observations and current data from satellites. They can identify weather patterns in order to create long-term forecasts with high precision.

Weather analytics is essential not only for knowing what you should wear tomorrow but also for predicting adverse weather conditions (hurricanes, strong winds, extremely high or low temperatures, etc.). Thus, ordinary people and municipality services can prepare in advance and avoid significant damages/losses.

6. Insurance

Working in the insurance industry means working with risks. This makes predictive analytics the best tool for this sector. Algorithms streamline the insurance claim approval process by reviewing previous claims and identifying risk factors. The process might take weeks when done manually; with smart analytics, it’s done automatically and instantly. This helps insurance companies correctly estimate future risks, as well as determine fraudulent claims in time and reject them, avoiding unreasonable expenses.

7. Financial Modeling

Financial planning is an essential part of any business regardless of the industry. Many financial teams are already using predictive analytics, or plan to do so, to foresee risks and revenues, allocate resources efficiently, optimize operations to avoid additional expenses, etc. Numerous finance management software applications have built-in predictive analytics features, proving that intelligent algorithms will in the future be widely used for financial services.

8. Social Media Analysis

For most brands, social media presence is a must nowadays as it’s the main communication channel with customers. The information generated in social media channels is precious for businesses if analyzed and used correctly. And there is no better way to do this than using predictive analytics tools. They help companies extract meaningful insights from customer comments and discussions, product reviews, numbers of likes/dislikes, etc., and make the necessary adjustments to their business processes.

9. Energy

The world electricity consumption growth will continue due to the proliferation of electric vehicles and renewable energy sources. That’s why the energy sector should look to scale production to meet the growing demand. Predictive analytics can help energy utilities create short- and long-term forecasts on energy demand, considering weather conditions, seasonality, new emerging consumers and other factors. Predictive maintenance is also an essential tool for the energy industry as it can reduce equipment failures. Consequently, companies avoid unexpected costs and customers get more stable energy supply services.

10. Human Resources

HR departments work with large volumes of people data, so they can also apply predictive analytics to their processes. Precisely, HR specialists can get forecasts concerning employee performance, staff turnover, the impact of various activities on employee engagement and more. Aggregated and analyzed data can reveal pain points in human resource management and help managers make data-driven designations to multiple positions. The result of workforce data analytics will be happier staff and improved productivity.

Conclusion

As you can see from our examples, both businesses and customers can benefit from predictive analytics applications. Undoubtedly, the technology is not easy to implement. But if you work with a reliable software provider, you won’t have to worry about successful outcomes.